Detection of electricity theft using data processing and LSTM method in distribution systems
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Sådhanå (2020) 45:286 https://doi.org/10.1007/s12046-020-01512-0
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Detection of electricity theft using data processing and LSTM method in distribution systems BEHC ¸ ET KOCAMAN1,*
¨ MEN2 and VEDAT TU
1
Department of Electrical and Electronics Engineering, Bitlis Eren University, Bitlis, Turkey Department of Computer Engineering, Bitlis Eren University, Bitlis, Turkey e-mail: [email protected]; [email protected]
2
MS received 16 April 2020; revised 6 October 2020; accepted 9 October 2020 Abstract. Electricity theft is a big problem faced by all energy distribution services and continues to rising. Therefore, studies on electricity theft detection techniques have increased in recent years. Unsuitable calibration and illegal calibration of energy meters during production may cause non-technical losses. Non-technical losses have been a major concern for the resulting security risks and the immeasurable loss of income. In most of the meter tampered locations, damaged meter terminals and/or illegal applications cannot be distinguishable during checking. In fact, electric distribution companies will never be able to eliminate electricity theft. But it is possible to take measure to detect, prevent and reduce it. In this paper, we developed by using deep learning methods on real daily electricity consumption data (Electricity consumption dataset of State Grid Corporation of China). Data reduction has been made by developing a new method to make the dataset more usable and to extract meaningful results. A Long Short-Term Memory (LSTM) based deep learning method has been developed for the dataset to be able to recognize the actual daily electricity consumption data of 2016. In order to evaluate the performance of the proposed method, the accuracy, prediction and recall metric was used by considering the five cross-fold technique. Performance of the proposed methods were found to be better than previously reported results. Keywords.
Electricity theft; non-technical loss; long short term memory.
1. Introduction In electricity consumption, very low technical losses occur due to the system and these losses are considered as maximum 5% of total consumption. Malicious consumers consuming electricity with different methods are called non-technical losses (NTL) or electric theft. Electric theft can lead to electricity unit prices, heavy load of electrical systems, huge loss of energy company and the dangers of public safety (such as fires and electric shocks). NTL behavior usually involves skipping the electricity meter, tampering the meter reading, or hacking the meter [1]. With the increase in energy needs on global based, the amount of consumption increased proportionally. An increase in energy prices affects the economy and increases the search for illegal ways for those responsible. Electricity theft rates differ in developed and developing countries. The average of this rate in OECD countries is about 7%. While the rate of electricity theft is 1–2% in developed co
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